I've been using for the last few weeks a fairly known setup/system which I have slightly modified to fit my requirements (intraday - as in intra half hour 
- index futures/cfd trading), and so far so good, that one seems to provide a real tangible edge, though I still need to improve on trade/risk/money management, eg a staged exit would probably yield even better results.
Anyhow, I have developed my own set of software tools (and market data) over the last couple of years and would like to backtest this setup under various market conditions to see how bad results can get - which they'll probably get at some point, let there be no doubt.
However what I'm missing at this point is a good, efficient but not necessarily super speedy (accuracy being more important than speed here) "peak detector" algorithm, ie a software routine that's able to identify swing hi/swing lo sequences among a time series data set (cfd/futures data stream).
Might sound easy, like duh just look for the Max/min value within a period, but in reality it's NOT. Noise, cycles, erratic behaviour of prices, etc... which are extremely well handled by our brain are but easy to detect and filter out.
In fact, the mere definition of what constitutes a valid/pertinent swing hi/lo is a task of its own already...
Has anyone looked into this, or better yet already using such a software algorithm? Which parameters do you specify to adjust the detection and filtering of data ? How does it work, on what platform ? Etc...
TIA.
- index futures/cfd trading), and so far so good, that one seems to provide a real tangible edge, though I still need to improve on trade/risk/money management, eg a staged exit would probably yield even better results.Anyhow, I have developed my own set of software tools (and market data) over the last couple of years and would like to backtest this setup under various market conditions to see how bad results can get - which they'll probably get at some point, let there be no doubt.
However what I'm missing at this point is a good, efficient but not necessarily super speedy (accuracy being more important than speed here) "peak detector" algorithm, ie a software routine that's able to identify swing hi/swing lo sequences among a time series data set (cfd/futures data stream).
Might sound easy, like duh just look for the Max/min value within a period, but in reality it's NOT. Noise, cycles, erratic behaviour of prices, etc... which are extremely well handled by our brain are but easy to detect and filter out.
In fact, the mere definition of what constitutes a valid/pertinent swing hi/lo is a task of its own already...
Has anyone looked into this, or better yet already using such a software algorithm? Which parameters do you specify to adjust the detection and filtering of data ? How does it work, on what platform ? Etc...
TIA.
